Thomas Dohmke, GitHub

Copilot Workspace is GitHub's take on AI-powered software engineering

by with No Comment Tech

Thomas Dohmke, GitHub

Image Credits: Vaughn Ridley/Sportsfile for Collision via Getty Images

Is the future of software development an AI-powered IDE? GitHub’s floating the idea.

Ahead of its annual GitHub Universe conference in San Francisco early this fall, GitHub announced Copilot Workspace, a dev environment that taps what GitHub describes as “Copilot-powered agents” to help developers brainstorm, plan, build, test and run code in natural language.

Jonathan Carter, head of GitHub Next, GitHub’s software R&D team, pitches Workspace as somewhat of an evolution of GitHub’s AI-powered coding assistant Copilot into a more general tool, building on recently introduced capabilities like Copilot Chat, which lets developers ask questions about code in natural language.

“Through research, we found that, for many tasks, the biggest point of friction for developers was in getting started, and in particular knowing how to approach a [coding] problem, knowing which files to edit and knowing how to consider multiple solutions and their trade-offs,” Carter said. “So we wanted to build an AI assistant that could meet developers at the inception of an idea or task, reduce the activation energy needed to begin and then collaborate with them on making the necessary edits across the entire corebase.”

At last count, Copilot had over 1.8 million paying individual and 50,000 enterprise customers. But Carter envisions a far larger base, drawn in by feature expansions with broad appeal, like Workspace.

“Since developers spend a lot of their time working on [coding issues], we believe we can help empower developers every day through a ‘thought partnership’ with AI,” Carter said. “You can think of Copilot Workspace as a companion experience and dev environment that complements existing tools and workflows and enables simplifying a class of developer tasks … We believe there’s a lot of value that can be delivered in an AI-native developer environment that isn’t constrained by existing workflows.”

There’s certainly internal pressure to make Copilot profitable.

Copilot loses an average of $20 a month per user, according to a Wall Street Journal report, with some customers costing GitHub as much as $80 a month. And the number of rival services continues to grow. There’s Amazon’s CodeWhisperer, which the company made free to individual developers late last year. There are also startups, like Magic, Tabnine, Codegen and Laredo.

Given a GitHub repo or a specific bug within a repo, Workspace — underpinned by OpenAI’s GPT-4 Turbo model — can build a plan to (attempt to) squash the bug or implement a new feature, drawing on an understanding of the repo’s comments, issue replies and larger codebase. Developers get suggested code for the bug fix or new feature, along with a list of the things they need to validate and test that code, plus controls to edit, save, refactor or undo it.

GitHub Workspace
Image Credits: GitHub

The suggested code can be run directly in Workspace and shared among team members via an external link. Those team members, once in Workspace, can refine and tinker with the code as they see fit.

Perhaps the most obvious way to launch Workspace is from the new “Open in Workspace” button to the left of issues and pull requests in GitHub repos. Clicking on it opens a field to describe the software engineering task to be completed in natural language, like, “Add documentation for the changes in this pull request,” which, once submitted, gets added to a list of “sessions” within the new dedicated Workspace view.

GitHub Workspace
Image Credits: GitHub

Workspace executes requests systematically step by step, creating a specification, generating a plan and then implementing that plan. Developers can dive into any of these steps to get a granular view of the suggested code and changes and delete, re-run or re-order the steps as necessary.

“If you ask any developer where they tend to get stuck with a new project, you’ll often hear them say it’s knowing where to start,” Carter said. “Copilot Workspace lifts that burden and gives developers a plan to start iterating from.”

GitHub Workspace
Image Credits: GitHub

Workspace enters technical preview on Monday, optimized for a range of devices, including mobile.

Importantly, because it’s in preview, Workspace isn’t covered by GitHub’s IP indemnification policy, which promises to assist with the legal fees of customers facing third-party claims alleging that the AI-generated code they’re using infringes on IP. (Generative AI models notoriously regurgitate their training datasets, and GPT-4 Turbo was trained partly on copyrighted code.)

GitHub says that it hasn’t determined how it’s going to productize Workspace, but that it’ll use the preview to “learn more about the value it delivers and how developers use it.”

I think the more important question is: Will Workspace fix the existential issues surrounding Copilot and other AI-powered coding tools?

An analysis of over 150 million lines of code committed to project repos over the past several years by GitClear, the developer of the code analysis tool of the same name, found that Copilot was resulting in more mistaken code being pushed to codebases and more code being re-added as opposed to reused and streamlined, creating headaches for code maintainers.

Elsewhere, security researchers have warned that Copilot and similar tools can amplify existing bugs and security issues in software projects. And Stanford researchers have found that developers who accept suggestions from AI-powered coding assistants tend to produce less secure code. (GitHub stressed to me that it uses an AI-based vulnerability prevention system to try to block insecure code in addition to an optional code duplication filter to detect regurgitations of public code.)

Yet devs aren’t shying away from AI.

In a StackOverflow poll from June 2023, 44% of developers said that they use AI tools in their development process now, and 26% plan to soon. Gartner predicts that 75% of enterprise software engineers will employ AI code assistants by 2028.

By emphasizing human review, perhaps Workspace can indeed help clean up some of the mess introduced by AI-generated code. We’ll find out soon enough as Workspace makes its way into developers’ hands.

“Our primary goal with Copilot Workspace is to leverage AI to reduce complexity so developers can express their creativity and explore more freely,” Carter said. “We truly believe the combination of human plus AI is always going to be superior to one or the other alone, and that’s what we’re betting on with Copilot Workspace.”

a screenshot showing Mintlify's website

Mintlify says customer GitHub tokens exposed in data breach

by with No Comment Tech

a screenshot showing Mintlify's website

Image Credits: TechCrunch

Documentation startup Mintlify says dozens of customers had GitHub tokens exposed in a data breach at the start of the month and publicly disclosed last week.

Mintlify helps developers create documentation for their software and source code by requesting access and tapping directly into the customer’s GitHub source code repositories. Mintlify counts fintech, database and AI startups as customers.

In a blog post Monday, Mintlify blamed its March 1 incident on a vulnerability in its own systems, but said 91 of its customers had their GitHub tokens compromised as a result.

These private tokens allow GitHub users to share their account access with third parties apps, including companies like Mintlify. If these tokens are stolen, an attacker could obtain the same level of access to a person’s source code as the token permits.

“The users have been notified, and we’re working with GitHub to identify whether the tokens were used to access private repositories,” Mintlify co-founder Han Wang wrote in a blog post.

News of the incident became public last week when some users on Reddit and Hacker News commented after getting an email from Mintlify on Friday about the incident, days after the company’s blog post initially told customers that “no further action is required on your part.”

In a post discussing the breach on Hacker News, Wang said a vulnerability in its systems was leaking the company’s internal admin credentials to customers. Those credentials could then be used to access the company’s internal endpoints to access other unspecified sensitive user information, Wang said.

Wang said that the company was in the process of deprecating the use of private tokens “to prevent an incident like this from ever happening again.”

While the blog post describes the person who discovered the vulnerability as a bug bounty reporter, the company’s co-founder Wang described the events as malicious.

“The targets of this attack were GitHub tokens of our users,” Wang told TechCrunch by email.

“Investigations with one impacted customer revealed that the leaked token was likely not used by the attacker. We are currently working with GitHub and our customers to uncover if any of the other tokens were used by the attacker,” Wang said.

Mintlify taps AI to automatically generate documentation from code

Programmer entering code on a laptop.

Exclusive: Eric Schmidt-backed Augment, a GitHub Copilot rival, launches out of stealth with $252M

by with No Comment Tech

Programmer entering code on a laptop.

Image Credits: scyther5 / Getty Images

AI is supercharging coding — and developers are embracing it.

In a recent StackOverflow poll, 44% of software engineers said that they use AI tools as part of their development processes now and 26% plan to soon. Gartner estimates that over half of organizations are currently piloting or have already deployed AI-driven coding assistants, and that 75% of developers will use coding assistants in some form by 2028.

Ex-Microsoft software developer Igor Ostrovsky believes that soon, there won’t be a developer who doesn’t use AI in their workflows. “Software engineering remains a difficult and all-too-often tedious and frustrating job, particularly at scale,” he told TechCrunch. “AI can improve software quality, team productivity and help restore the joy of programming.”

So Ostrovsky decided to build the AI-powered coding platform that he himself would want to use.

That platform is Augment, and on Wednesday it emerged from stealth with $252 million in funding at a near-unicorn ($977 million) post-money valuation. With investments from former Google CEO Eric Schmidt and VCs including Index Ventures, Sutter Hill Ventures, Lightspeed Venture Partners, Innovation Endeavors and Meritech Capital, Augment aims to shake up the still-nascent market for generative AI coding technologies.

“Most companies are dissatisfied with the programs they produce and consume; software is too often fragile, complex and expensive to maintain with development teams bogged down with long backlogs for feature requests, bug fixes, security patches, integration requests, migrations and upgrades,” Ostrovsky said. “Augment has both the best team and recipe for empowering programmers and their organizations to deliver high-quality software quicker.”

Ostrovsky spent nearly seven years at Microsoft before joining Pure Storage, a startup developing flash data storage hardware and software products, as a founding engineer. While at Microsoft, Ostrovsky worked on components of Midori, a next-generation operating system the company never released but whose concepts have made their way into other Microsoft projects over the last decade.

In 2022, Ostrovsky and Guy Gur-Ari, previously an AI research scientist at Google, teamed up to create Augment’s MVP. To fill out the startup’s executive ranks, Ostrovsky and Gur-Ari brought on Scott Dietzen, ex-CEO of Pure Storage, and Dion Almaer, formerly a Google engineering director and a VP of engineering at Shopify.

Augment remains a strangely hush-hush operation.

In our conversation, Ostrovsky wasn’t willing to say much about the user experience or even the generative AI models driving Augment’s features (whatever they may be) — save that Augment is using fine-tuned “industry-leading” open models of some sort.

He did say how Augment plans to make money: standard software-as-a-service subscriptions. Pricing and other details will be revealed later this year, Ostrovsky added, closer to Augment’s planned GA release.

“Our funding provides many years of runway to continue to build what we believe to be the best team in enterprise AI,” he said. “We’re accelerating product development and building out Augment’s product, engineering and go-to-market functions as the company gears up for rapid growth.”

Rapid growth is perhaps the best shot Augment has at making waves in an increasingly cutthroat industry.

Practically every tech giant offers its own version of an AI coding assistant. Microsoft has GitHub Copilot, which is by far the firmest entrenched with over 1.3 million paying individual and 50,000 enterprise customers as of February. Amazon has AWS’ CodeWhisperer. And Google has Gemini Code Assist, recently rebranded from Duet AI for Developers.

Elsewhere, there’s a torrent of coding assistant startups: Magic, Tabnine, Codegen, Refact, TabbyML, Sweep, Laredo and Cognition (which reportedly just raised $175 million), to name a few. Harness and JetBrains, which developed the Kotlin programming language, recently released their own. So did Sentry (albeit with more of a cybersecurity bent). 

Can they all — plus Augment now — do business harmoniously together? It seems unlikely. Eye-watering compute costs alone make the AI coding assistant business a challenging one to maintain. Overruns related to training and serving models forced generative AI coding startup Kite to shut down in December 2022. Even Copilot loses money, to the tune of around $20 to $80 a month per user, according to The Wall Street Journal.

Ostrovsky implies that there’s momentum behind Augment already; he claims that “hundreds” of software developers across “dozens” of companies, including payment startup Keeta (which is also Eric Schmidt-backed), are using Augment in early access. But will the uptake sustain? That’s the million-dollar question, indeed.

I also wonder if Augment has made any steps toward solving the technical setbacks plaguing code-generating AI, particularly around vulnerabilities.

An analysis by GitClear, the developer of the code analytics tool of the same name, found that coding assistants are resulting in more mistaken code being pushed to codebases, creating headaches for software maintainers. Security researchers have warned that generative coding tools can amplify existing bugs and exploits in projects. And Stanford researchers have found that developers who accept code recommendations from AI assistants tend to produce less secure code.

Then there’s copyright to worry about.

Augment’s models were undoubtedly trained on publicly available data, like all generative AI models — some of which may’ve been copyrighted or under a restrictive license. Some vendors have argued that fair use doctrine shields them from copyright claims while at the same time rolling out tools to mitigate potential infringement. But that hasn’t stopped coders from filing class action lawsuits over what they allege are open licensing and IP violations.

To all this, Ostrovsky says: “Current AI coding assistants don’t adequately understand the programmer’s intent, improve software quality nor facilitate team productivity, and they don’t properly protect intellectual property. Augment’s engineering team boasts deep AI and systems expertise. We’re poised to bring AI coding assistance innovations to developers and software teams.”

Augment, which is based in Palo Alto, has around 50 employees; Ostrovsky expects that number to double by the end of the year.

Thomas Dohmke, GitHub

Copilot Workspace is GitHub's take on AI-powered software engineering

by with No Comment Tech

Thomas Dohmke, GitHub

Image Credits: Vaughn Ridley/Sportsfile for Collision via Getty Images

Is the future of software development an AI-powered IDE? GitHub’s floating the idea.

Ahead of its annual GitHub Universe conference in San Francisco early this fall, GitHub announced Copilot Workspace, a dev environment that taps what GitHub describes as “Copilot-powered agents” to help developers brainstorm, plan, build, test and run code in natural language.

Jonathan Carter, head of GitHub Next, GitHub’s software R&D team, pitches Workspace as somewhat of an evolution of GitHub’s AI-powered coding assistant Copilot into a more general tool, building on recently introduced capabilities like Copilot Chat, which lets developers ask questions about code in natural language.

“Through research, we found that, for many tasks, the biggest point of friction for developers was in getting started, and in particular knowing how to approach a [coding] problem, knowing which files to edit and knowing how to consider multiple solutions and their trade-offs,” Carter said. “So we wanted to build an AI assistant that could meet developers at the inception of an idea or task, reduce the activation energy needed to begin and then collaborate with them on making the necessary edits across the entire corebase.”

At last count, Copilot had over 1.8 million paying individual and 50,000 enterprise customers. But Carter envisions a far larger base, drawn in by feature expansions with broad appeal, like Workspace.

“Since developers spend a lot of their time working on [coding issues], we believe we can help empower developers every day through a ‘thought partnership’ with AI,” Carter said. “You can think of Copilot Workspace as a companion experience and dev environment that complements existing tools and workflows and enables simplifying a class of developer tasks … We believe there’s a lot of value that can be delivered in an AI-native developer environment that isn’t constrained by existing workflows.”

There’s certainly internal pressure to make Copilot profitable.

Copilot loses an average of $20 a month per user, according to a Wall Street Journal report, with some customers costing GitHub as much as $80 a month. And the number of rival services continues to grow. There’s Amazon’s CodeWhisperer, which the company made free to individual developers late last year. There are also startups, like Magic, Tabnine, Codegen and Laredo.

Given a GitHub repo or a specific bug within a repo, Workspace — underpinned by OpenAI’s GPT-4 Turbo model — can build a plan to (attempt to) squash the bug or implement a new feature, drawing on an understanding of the repo’s comments, issue replies and larger codebase. Developers get suggested code for the bug fix or new feature, along with a list of the things they need to validate and test that code, plus controls to edit, save, refactor or undo it.

GitHub Workspace
Image Credits: GitHub

The suggested code can be run directly in Workspace and shared among team members via an external link. Those team members, once in Workspace, can refine and tinker with the code as they see fit.

Perhaps the most obvious way to launch Workspace is from the new “Open in Workspace” button to the left of issues and pull requests in GitHub repos. Clicking on it opens a field to describe the software engineering task to be completed in natural language, like, “Add documentation for the changes in this pull request,” which, once submitted, gets added to a list of “sessions” within the new dedicated Workspace view.

GitHub Workspace
Image Credits: GitHub

Workspace executes requests systematically step by step, creating a specification, generating a plan and then implementing that plan. Developers can dive into any of these steps to get a granular view of the suggested code and changes and delete, re-run or re-order the steps as necessary.

“If you ask any developer where they tend to get stuck with a new project, you’ll often hear them say it’s knowing where to start,” Carter said. “Copilot Workspace lifts that burden and gives developers a plan to start iterating from.”

GitHub Workspace
Image Credits: GitHub

Workspace enters technical preview on Monday, optimized for a range of devices, including mobile.

Importantly, because it’s in preview, Workspace isn’t covered by GitHub’s IP indemnification policy, which promises to assist with the legal fees of customers facing third-party claims alleging that the AI-generated code they’re using infringes on IP. (Generative AI models notoriously regurgitate their training datasets, and GPT-4 Turbo was trained partly on copyrighted code.)

GitHub says that it hasn’t determined how it’s going to productize Workspace, but that it’ll use the preview to “learn more about the value it delivers and how developers use it.”

I think the more important question is: Will Workspace fix the existential issues surrounding Copilot and other AI-powered coding tools?

An analysis of over 150 million lines of code committed to project repos over the past several years by GitClear, the developer of the code analysis tool of the same name, found that Copilot was resulting in more mistaken code being pushed to codebases and more code being re-added as opposed to reused and streamlined, creating headaches for code maintainers.

Elsewhere, security researchers have warned that Copilot and similar tools can amplify existing bugs and security issues in software projects. And Stanford researchers have found that developers who accept suggestions from AI-powered coding assistants tend to produce less secure code. (GitHub stressed to me that it uses an AI-based vulnerability prevention system to try to block insecure code in addition to an optional code duplication filter to detect regurgitations of public code.)

Yet devs aren’t shying away from AI.

In a StackOverflow poll from June 2023, 44% of developers said that they use AI tools in their development process now, and 26% plan to soon. Gartner predicts that 75% of enterprise software engineers will employ AI code assistants by 2028.

By emphasizing human review, perhaps Workspace can indeed help clean up some of the mess introduced by AI-generated code. We’ll find out soon enough as Workspace makes its way into developers’ hands.

“Our primary goal with Copilot Workspace is to leverage AI to reduce complexity so developers can express their creativity and explore more freely,” Carter said. “We truly believe the combination of human plus AI is always going to be superior to one or the other alone, and that’s what we’re betting on with Copilot Workspace.”

GitHub Copilot gets extensions

by with No Comment Tech

Image Credits: David Paul Morris/Bloomberg / Getty Images

Build is Microsoft’s largest developer conference and of course, it’s all about AI this year. So it’s no surprise that GitHub’s Copilot, GitHub’s “AI pair programming tool,” is taking center stage for a bit today with the launch of Copilot Extension. As the name suggests, Copilot Extensions allow developers to extend Copilot with third-party skills.

Today’s launch partners cover a wide variety of skills. They include DataStax, Docker, LambdaTest, LaunchDarkly, McKinsey & Company, Microsoft Azure and Teams, MongoDB, Octopus Deploy, Pangea, Pinecone, Product Science, ReadMe, Sentry and Stripe.

“Our goal: make GitHub Copilot the most integrated, powerful, intelligent AI platform there is — with unlimited possibilities to accelerate human progress,” said GitHub’s SVP of Product Mario Rodriguez. “Programming in natural language will continue to lower the barrier to entry for anyone who wants to build software. Today, we are closer to a future where one billion people can build on GitHub, with Copilot as an intelligent platform that integrates with any tool in the developer tech stack, entirely in natural language.”

These extensions will live in the GitHub Marketplace, but developers will also be able to create their own private extensions to integrate with their internal systems and APIs.

Image Credits: GitHub/Microsoft

The idea here, of course, is to help developers stay in their flow and interact with these systems in natural language and without having to switch context. For some partners, that means accessing their documentation in Copilot, while for others, it includes taking actions. Users of the Octopus deployment tool, for example, will be able to use an extension to check on the state of their deployments, while Sentry users will be able to resolve issues with their deployment pipelines in natural language and DataStax users will be able to interact with their databases.

As of now, Copilot Extensions are in private preview.

We’re launching an AI newsletter! Sign up here to start receiving it in your inboxes on June 5.